It is known that various computer-based systems and computer-implemented methodologies can be used to generate multi-dimensional surface models of geometric structures, such as, for example, anatomic structures. More specifically, a variety of systems and methods have been used to generate multi-dimensional surface models of the heart and/or particular portions thereof.
One conventional methodology or technique involves the generation of a plurality of individual surface models corresponding to different regions of interest of a particular structure, and then joining the individual surface models together to form a single composite multi-dimensional surface model. It is known to generate the individual surface models by collecting location data points from the surfaces and volumes enclosed by the surfaces of the respective regions of interest and then using those location data points to generate an individual surface model for each region of interest.
Any number of techniques can be used to generate the individual surface models from the respective location data points, including, for example, convex hull, star-shaped domain approximation, and alpha-shape techniques. Conventional techniques for generating composite surface models are not without their drawbacks, however. For example, individual surface models may not reflect the corresponding region of interest with a desired degree detail or accuracy, or the surface models may be less than ideal for multi-dimensional Boolean operations. Either one of these drawbacks may result in a composite surface model that does not reflect the structure of interest with the desired degree of accuracy. Moreover, at least some conventional techniques are computationally intensive, and may take a relatively long time and/or require relatively large processing resources.
Thus, using at least some known techniques, surface models that are formed using collections of location data points may not provide the desired degree of accuracy and/or may require an undesirable amount of additional processing that increases the complexity of, and the length of time required to perform, the surface model generation process.